Cooperative bargaining game‐theoretic methodology for 5G wireless heterogeneous networks
Bibliographic record
Abstract
ABSTRACT Cooperative game‐theoretic modelling, analysis and design are critical to mitigate interference and save energy for 5G wireless evolution. Nash axiomatic cooperative game has been widely used to model various cooperation‐motivated technical problems, notably in signal processing and communications. However, its most potentials have not been fully exploited, for example, different trade‐offs between efficiency and fairness, where efficiency is referred to as both spectral efficiency (SE) and energy efficiency (EE). The trade‐offs can be determined by various cooperative solution concepts, for example, the favourable Nash bargaining solution and its rarely studied extensions. Therefore, we first overview the basics of the celebrated Nash bargaining solution and its extensions with geometric interpretations to help better understand them and facilitate distributed algorithm design. Then, both symmetric and asymmetric cooperative game‐theoretic frameworks are formulated with different trade‐offs incorporating an asymmetric unified β‐coefficient determined cooperative game model. As a use case, an α‐parameter‐related preference function is designed first incorporating both SE and EE. Then, the presented frameworks with the new preference function are studied in a typical heterogeneous network. In the following text, we characterise the effects of β‐coefficient to fairness and efficiency and α‐parameter to SE and EE. Finally, we conclude the article with the hope of stimulating more interest in cooperative bargaining game and its wider applications in the signalling and communication communities. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".